Mass Spectrometry
A mass spectrometer (MS) is a device for measuring the mass-to-charge ratio (m/Q) of ions. It can be used for chemical analysis. All types of MS operate by subjecting charged, gas-phase molecules or atoms (ions) to electric and/or magnetic fields within a reduced pressure (vacuum) environment.
Mass spectrometers are commonly used for chemical analysis of gaseous, liquid, solid and plasma samples in a broad range of disciplines.
Samples that do not originate in the gas phase must be converted to the gas phase (vaporization or desorption) before analysis.
Further, the molecules of the sample (analyte) must be given a charge (ionized) prior to analysis. Vaporization (if necessary) and ionization of the sample can take place in devices separate from the mass analyzer. Numerous techniques exist for vaporization and ionization of samples.
For a given sample, a MS generally records data for several chemical species corresponding to a broad range of m/Q. Data are often presented as “spectrum” of observed signal intensity as a function of m/Q, called a mass spectrum. In the digital age, this spectrum is represented by a histogram, e.g. series of digital values which closely represents the (continuous) spectrum.
The mass of an ion is a function of the specific atom(s) comprising the ion. For instance the most abundant water isotopologue cation, H216O+, has a mass of 18.01 Dalton (1 Da=m(12C)/12=1.66×10−27 kg), which is the sum of the masses of 2 hydrogen atoms and 1 oxygen-16 atom minus one electron. With a net charge of 1 elementary charge e (e=atomic charge unit=1.602×10−19 coulomb), this cation has m/Q=18.01 thomson (Th).
The mass spectrum of a sample can be used to deduce the identity of the molecules in the sample based on the observed m/Q value(s). For cases where the response of the MS can be appropriately calibrated, MS data can also quantify the concentration of specific molecules within the sample.
The disclosed invention relates to types of MS producing a large number of spectra in short time, in particular fast mass spectrometers providing 1'000 spectra per second or more. A prominent example is the time-of-flight mass spectrometer (TOFMS). This includes the recently proposed distance-of-flight mass spectrometers (DOFMS) or electrostatic ion traps. In the following, the invention is described in the context of a TOFMS.
A TOFMS includes a TOF analyzer (TOF 1) that determines the m/Q of an ion by measuring the time required for that ion to travel a known distance 2 after ions are accelerated to a known kinetic energy or by a known impulse 3, called an extraction. For any ion in a TOF the observed ion time-of-flight will be approximately proportional to the square root of the ion's m/Q. FIG. 1 shows a typical TOFMS.
Data Acquisition
The kHz extractions of the TOF mass spectrometer are generally triggered by an external timing generator 4.
The timing generator is an electronic device (stand-alone or PC component) capable producing high frequency triggers (digital outputs 5) with high temporal precision.
TOF extractions may run continuously and freely or they may be configured to occur simultaneous to some external process 6, such as the changing of a sample or a pulsed ionization event. To achieve such synchronization, the timing generator may also receive external triggers (inputs 7) and can be programmed to output triggers 5 relative to these input triggers.
TOF mass spectrometers typically detect the presence of ions using microchannel plate (MCP) detectors 8. When struck by an ion these detectors output a detectable voltage 9. The flight time of an ion is the time between the extraction event and the moment that ion strikes the MCP.
In order to measure the flight times of ions with high precision, TOF mass spectrometers typically use time-to-digital or analog-to-digital converters (TDC and ADC, respectively) with GHz or faster sampling rates (nanosecond of sub-nanosecond precision). These digitizers 10 convert the voltage output by the MCP to a digital value 11 that can be saved in a computer 12.
As an example, U.S. Pat. No. 6,707,411 B1 (Agilent) discloses an ADC with on-chip memory. The ADC is structured to generate digital samples at a sampling rate. At least one of a data output of the memory, a data output bus and an output port is structured to operate at a maximum rate less than the sampling rate. The ADC may include a sample processor to reduce the rate at which received digital samples are conveyed to the memory, furthermore, the samples may be read out from the memory at a rate less than the sampling rate.
Accurate recording of an ion's flight time requires synchronization of the digitizer 10 with the TOF extraction events. This synchronization is generally managed by the timing generator, which outputs a simultaneous trigger at output 5 to the digitizer and the TOF. In some cases, the timing generator is a component of the digitizer.
In most configurations, the digitizer records a continuous stream of values beginning at the moment of the extraction and extending for some period less than or equal to the TOF extraction period. This waveform represents the mass spectrum of the sample entering the mass spectrometer during that extraction. Graphically, it is typically presented as a histogram of values (intensity vs time of flight) 16. For the purposes of the data acquisition (DAQ), the waveform is best thought of as a 1-dimensional array 17 (see FIG. 2).
TOF analyzers potentially produce a complete spectrum for every TOF extraction. A typical TOF extraction rate is 10 to 200 kHz. This means TOFMS are capable of recording fast processes down to a 5 μs time scale. Such fast monitoring produces a large amount of data which may be too large for PC based data acquisition.
Processes that are slower than the TOF extraction rate can be observed by accumulating (or averaging) many consecutive TOF extractions in a segment 18 of the memory 19 of the digitizer 10 (see FIG. 3).
This so called waveform averaging 20 (see FIG. 4) reduces the total amount of data. For example a process can be monitored with a 1−s time resolution, thereby allowing the waveforms of 50'000 TOF extractions to be averaged into a single summed spectrum. This reduces the data load for at least a factor of 10'000.
For the TOF to resolve (observe) changes in chemical composition, the DAQ system must record and save data at a rate (average spectra/sec) equal to or greater than the changes of interest.
In theory, the maximum continuous save rate (MCSR) is equal to the TOF extraction frequency. In this case, no averaging would be employed, and the data corresponding to each TOF extraction would be saved.
In practice, the MCSR is determined by technical specifications of the DAQ hardware.
In the most efficient DAQ systems, waveform averaging is performed in the memory of the digitizer (see FIG. 5). After the defined number of TOF extractions have been waveform averaged in memory, the averaged waveform 21 is transferred 22 from the digitizer memory 19 to PC RAM 13 and eventually saved (step 23) to the hard disk 14 (cf. FIG. 1). We refer to this transfer and save as the processing step 24.
Because acquisition may be idle during some or all of the transfer step, the achieved continuous save rate, which is the inverse of the time 25 between successive save events, is affected by the rates at which each average spectrum can be transferred to the PC and saved to disk (cf. FIG. 6).
The significance of the time required to write data to the hard drive depends on the architecture of the data acquisition software (e.g., employment of multiple threads); for most modern applications it only needs to be considered at extremely high save rates.
For simplicity, we consider the case of a digitizer with a single memory buffer, such that acquisition is completely idle during the transfer step. And we introduce the term idle time to describe the duration of the transfer step and any other time latencies associated with the processing of each averaged dataset.
In this case, the continuous save time 25 is the sum of the averaging time 26 and the idle time 27. And the save efficiency, which is the fraction of the continuously running TOF extractions that are saved, is the ratio of the averaging time 26 to the continuous save time 25.
In the most efficient scenarios (acquisition regime 28) the idle time is negligible compared to the averaging time. Here, save rates (average mass spectra/sec) can be increased by reducing averaging time with little cost to efficiency.
As save rates are increased, a low efficiency regime (acquisition regime 29) is reached, where averaging times are short relative to idle times. In this regime, decreases in averaging time reduce efficiency linearly, but have little effect on save rates. Save rates (average mass spectra/sec) effectively plateau at the inverse of the idle time.
This point at which the acquisition rate plateaus is the maximum continuous save rate (MCSR). For instance, if transfer of data requires 500 microseconds and the digitizer is idle during this time, the MCSR is 1/500 microseconds=2000 kHz.
The MCSR of an analog-to-digital converter (ADC)—based system is often slower than the TOF extraction frequency, whereas time-to-digital converters (TDC)—based systems have MCSR approaching or equal to the TOF extraction frequency. This difference is related to the larger size (bytes) of data points recorded by the ADC and the longer time required for transfer and save of these larger values.
Continuous Samples
Some MS experiments make a single measurement of a single sample, in order to determine its instantaneous chemical composition. In these cases, data acquisition rates are irrelevant. The experimenter can average data for any duration less than or equal to the amount of time the steady-state sample produces ions.
Other MS experiments make successive, time-resolved measurements of a single sample, in order to monitor how the composition of that sample changes in time. An example of this is the measurement of the concentrations of gases in ambient air. Changes of interest may vary on timescales ranging from 1 microsecond to longer.
MS spectra should be saved at a rate greater than or equal to the rate of changes interest. Below this rate, dynamic changes in ion intensities will be averaged and not resolved. For example, see FIG. 7 which shows measurements (recorded signals 31, 32) of a continuous ion intensity signal 30 at two different save rates, corresponding to segments 20 of different lengths.
For experiments recording successive spectra to monitor changes in a single sample, the save efficiency is approximately 100% for data acquisition with waveform averaging at rates less than or equal to MCSR.
Observations of phenomena changing at rates faster than the MCSR cannot be made continuously. Instead, they can only be made in discontinuous bursts (Methods for accomplishing this are described later in the next section).
Discontinuous Samples
Other MS experiments make successive measurements of different samples, in order to compare the composition of the different samples. Some finite time exists between the measurements of successive samples.
The changing of the sample may be controlled by the experimenter. An example is the movement of a pulsed ionization laser across a surface in order to compare composition at different positions.
Alternatively, the changing of the sample may be driven by some sporadic external phenomena. An example is the measurement of the mass spectra of individual ambient aerosol particles, where particles are sampled from the air into the mass spectrometer.
In some cases, the experiment aims only to measure the steady-state chemical composition of each sample. In this case a single average mass spectrum is recorded for each sample.
In this steady-state case, the required rate of data acquisition depends on how rapidly the sample is changing, i.e., how much time exists between successive samples.
Data may be acquired continuously with waveform averaging across the duration of the entire sequence of samples, provided the waveform averaging can be done at a rate faster than the changing of the samples. i. e., provided the sample is changing at a rate below the MCSR. See, for example, FIG. 8 which shows the resolution of three discrete samples (ion intensities 33) resolved with continuous waveform averaging yielding the recorded signal 34. The samples are able to be resolved because they enter the mass spectrometer at a rate well below the averaging rate.
Alternatively, acquisition of a single average spectrum may be synchronized with the production/ionization of each sample.
For cases where the experimenter controls the changing of the sample, this synchronization is relatively straightforward. For instance, a single average spectrum may be acquired following each firing of an ionization laser. Such acquisition is shown in FIG. 9. The external triggers 35 relating to the ionization impulses are input to the digitizer in order to synchronize discontinuous waveform averaging. The triggers may be periodic, however this is not compulsory. The discontinuous ion signal 36 is correlated with the triggers 35, the averaging into segments 20 is shown in time line 37, yielding signal 38.
For cases where the changing of the sample is sporadic, synchronization requires some external measurement to determine the presence of a sample. For instance, for ambient aerosol particles being sampled into a mass spectrometer, one may detect the presence of a particle in the inlet of the mass spectrometer via a light scattering measurement. Acquisition of the mass spectrum is then triggered when light scattering signal is detected. Many single particle mass spectrometers operate on this principal.
An alternative has been proposed in P. F. DeCarlo, “Field-Deployable, High-Resolution, Time-of-Flight Aerosol Mass Spectrometer” (Anal. Chem., Vol. 78, No. 24, December 2006, 8281), namely a so-called “brute-force single-particle (BFSP) mode”. According to that proposal, a single chopper cycle obtained prior to ionization is captured and transferred without prior averaging to computer memory. After transfer to memory, the data is filtered with user-defined, single-particle signal thresholds on multiple values of m/Q or combinations of values of m/Q, allowing the identification of single particle events and recording full mass spectra of these events. However, due to the high overhead for transferring large amounts of data from the ADC to the computer memory through a PCI bus, the duty cycle was very low. A slight improvement of the duty cycle was achieved by on-board data compression.
In other cases, the experiment aims to measure time-varying changes in the composition of each sample. In this case, multiple successive mass spectra are recorded for each sample.
For cases where the time-varying changes of interest in each sample are slower than the MCSR, it is possible to acquire data continuously in waveform averaging mode across the duration of the entire sequence of samples.
Alternatively, a second, discontinuous averaging mode exists that enables short bursts of acquisition at rates greater than the MCSR. For example, a quick succession of mass spectra could be collected following each pulse of the ionization laser.
In this block averaging mode, which is detailed in FIG. 10, the memory buffer 19 of the digitizer is configured to have multiple segments 18 (in contrast to the single segment used in waveform averaging).
For instance, a process of interest with total duration of 1 ms can be recorded into a 20-segment block, where 20 successive TOF extractions of 50 us each are written into the 20 unique segments without averaging. Following acquisition of this block, the system goes idle while the data block is processed (see FIG. 11), i. e. during the processing step 24 including the transfer 22 of the data in the digitizer memory 19 to RAM 13 as well as saving 23 the data to the computer hard drive 14. The advantage here is that there is no dead-time for transfer between the acquisitions of each extraction. Instead, the dead time occurs after the acquisition of the extractions of interest. This enables the recording of a burst of successive TOF spectra with an effective save rate greater than the MCSR.
FIG. 12 demonstrates the application of block averaging to the laser ionization example of FIG. 9. Note that with block averaging 40 yielding corresponding segments 39, the decay of signal for each sample is resolved as can be seen from the recorded signal 41.
With block averaging 39, it is also possible to average successive waveforms in a single segment. This is detailed in FIG. 13. For example, the 1 ms event just described could also be recorded in a 10-segment block where 20 successive TOF extractions of 50 us each are written into the segments by averaging 2 waveforms per segment (e.g., segment 1 is the average of waveforms 1 and 2).
Note that waveform averaging is equivalent to block averaging with one segment per block.
For experiments making measurements of many samples, one may maintain 100% acquisition efficiency by synchronizing the sample change with the data acquisition blocks. Using the example from above: The pulsing of the ionization laser being used to compare different positions on the surface would be synchronized with the start of data acquisition blocks.
For experiments making measurements of many samples, where the experimenter does not control the changing of the sample, the experimenter has three choices:    (i) Continuously acquire waveform average data below the MCSR, thereby maintaining high acquisition efficiency. As shown in FIG. 8, this method succeeds if the changes of interest (sample change or change in single sample) are slower than MCSR. Using the example from above: Individual ambient aerosols are being sampled into the mass spectrometer at a rate (particles/s) lower than the MCSR. FIG. 14 shows the case where the rate of sample occurrence 42 is much higher than the acquisition rate (time line 43). As can be seen from the recorded signal 44, ions from all/most samples are measured, but the individual samples are not resolved.    (ii) Continuously block average data or waveform average at a rate above the MCSR. This method allows the resolution of more rapidly changing samples, but risks missing many samples, an effect that increases with increased acquisition rate. Using the example from above: Individual ambient aerosols being sampled into the mass spectrometer (particles/s) are only measured if they are sampled during an acquisition event; they are missed if they are sampled during a process event. This is demonstrated in FIG. 15, where the samples 42 from FIG. 14 are measured with block averaging (time line 45). As can be seen from the recorded signal 46, individual samples are resolved, buy many are missed because of the significant idle times.    (iii) Acquire data in the block mode, where each data acquisition block is triggered by some external measurement that detects the presence of a sample. Extending the example from above: An individual ambient aerosol particle is detected by a non-destructive optical measurement technique upstream of the mass spectrometer, thereby triggering the start of a mass spectrometer data acquisition block. This method requires that the samples of interest are detectable by a non-destructive method that is compatible with the MS sampling system. Efficiency is derived from the fact that time is not wasted processing mass spectra that do not contain information of interest. The extent of this efficiency gain depends on the rate at which samples enter the mass spectrometer. At low rates, efficiency can approach 100%. At high rates, where all spectra have information of interest, there is no gain.
It is apparent that each of the three approaches has its downsides and that there are situations where the quality of the obtained measurements is compromised in all the three cases.